In the field of image processing, there are several problems where an efficient search of the
solutions has to be performed within a complex search domain to find an optimal solution …

Multilevel thresholding is the method applied to segment the given image into unique sub-
regions when the gray value distribution of the pixels is not distinct. The segmentation …

Abstract The Kapur and Otsu methods are widely used image thresholding approaches and
they are very efficient in bi-level thresholding applications. Evolutionary algorithms have …

In image processing, the maximum entropy principle is generally recognized as having a
relevant role in the initial part of image elaboration. The first step of processing in fact, sees …

Abstract Multi-Objective Evolutionary Algorithms (MOEAs) are known to solve problems
where two or more conflicting goals are involved. To accomplish it, MOEAs incorporate …

The maximum entropy principle has a relevant role in image processing, in particular for
thresholding and image segmentation. Different entropic formulations are available to this …

The essential tool in image processing, computer vision and machine vision is edge
detection, especially in the fields of feature extraction and feature detection. Entropy is a …

Image segmentation involves identifying the distinct objects present in an image based on
the properties such as intensity, color, texture etc. There are many image segmentation …

Multilevel thresholding is one of the effective image segmentation methods. However, it
faces three big challenges:(1) how to adaptively determine the number of multiple …

Image segmentation, the process of partitioning an image into meaningful regions, is a
fundamental step in image processing, crucial for applications like computer vision, medical …